1. Supervised Learning: Perceptrons and Backpropagation

2.  Connectionist /ism==  Parallel Distributed Processing (PDP)

3.  Intelligence is emergent

13.  Used to train multilayer feedforward networks

15.  Assumes a continuous activation function

17.  Used to train multilayer feedforward networks  Assumes a continuous activation function  Delta rule

18.  Perceptron update rule was:  Backprop update rule is:

19.  Error of an output node:

20.  Error of a hidden node:

25.  demo

27.  Encoding / Feature Extraction  # neurons used  # layers used  Output mapping

28.  Classification

29.  Pattern Recognition

30.  Classification  Pattern Recognition  Content Addressable Memory

31.  Classification  Pattern Recognition  Content Addressable Memory  Prediction

32.  Classification  Pattern Recognition  Content Addressable Memory  Prediction  Optimization

33.  Classification  Pattern Recognition  Content Addressable Memory  Prediction  Optimization  Filtering

34.  Degrade gracefully

35.  Solve ill-defined problems

36.  Degrade gracefully  Solve ill-defined problems  Flexible

37.  Degrade gracefully  Solve ill-defined problems  Flexible  Generalization

38.  Time & Memory

39.  Black box

40.  Time & Memory  Black box  Trial and Error

41.  If you can draw a flow chart or formula

42.  If a piece of hardware or software already exists that does what you want

43.  If you can draw a flow chart or formula  If a piece of hardware or software already exists that does what you want  If you want to functionality to evolve

44.  If you can draw a flow chart or formula  If a piece of hardware or software already exists that does what you want  If you want to functionality to evolve  Precise answers are required

45.  If you can draw a flow chart or formula  If a piece of hardware or software already exists that does what you want  If you want to functionality to evolve  Precise answers are required  The problem could be described in a lookup table

46.  You can define a correct answer

47.  You have a lot of training data with examples of right and wrong answers

48.  You can define a correct answer  You have a lot of training data with examples of right and wrong answers  You have lots of data but can’t figure how to map it to output

49.  You can define a correct answer  You have a lot of training data with examples of right and wrong answers  You have lots of data but can’t figure how to map it to output  The problem is complex but solvable

50.  You can define a correct answer  You have a lot of training data with examples of right and wrong answers  You have lots of data but can’t figure how to map it to output  The problem is complex but solvable  The solution is fuzzy or might change slightly

51.  2007 Rechnender Raum’s Inverted MachineInverted Machine

52.  Jonathan McCabe’s  Nervous States 2006  Each pixel is the Output state of a Neural network given Different inputs

53.  2007 Phillip Stearns AANN: Artificial Analog Neural Network

55.  Ted?